Information Handling System and Computer Program Product for Deducing Entity Relationships Across Corpora Using Cluster Based Dictionary Vocabulary Lexicon

ABSTRACT

An approach is provided for identifying entity relationships based on word classifications extracted from business documents stored in a plurality of corpora. In the approach, performed by an information handling system, a plurality of cluster classifications are identified for the business documents so that entity information from the business documents can be classified or assigned to the cluster classifications, such as by performing natural language processing (NLP) analysis of the business documents. The approach applies semantic analysis to identify and score entity relationships between the entity information classified in the cluster classifications, and based on the scored entity relationships, cluster relationships between the cluster classifications are identified.

BACKGROUND OF THE INVENTION

In the field of artificially intelligent computer systems capable ofanswering questions posed in natural language, cognitive questionanswering (QA) systems (such as the IBM Watson™ artificially intelligentcomputer system or and other natural language question answeringsystems) process questions posed in natural language to determineanswers and associated confidence scores based on knowledge acquired bythe QA system. In operation, users submit one or more questions througha front-end application user interface (UI) or application programminginterface (API) to the QA system where the questions are processed togenerate answers that are returned to the user(s). In order to preparean answer, traditional QA systems use a named entity recognition (NER)process (also known as entity identification, entity chunking and entityextraction) to analyze textual information in a large knowledge database(or “corpus”) by locating and classifying elements in the textualinformation into pre-defined categories, such as the names of persons,organizations, locations, expressions of times, quantities, monetaryvalues, percentages, etc. However, the NER processes used in differentindustry domains often results in different or conflicting named entityextraction results, depending on the contexts of the different industrydomains. For example, the same entity may be identified in differentindustry domains with the same identifier or with different identifiers,again depending on the contexts of the different industry domains. Whilecertain named entity extraction schemes have been proposed which extractthe named entities of a class from a large amount of corpus and into adictionary for a given industry domain, such schemes are not well suitedfor contextualizing the recognition named entities extracted fromdifferent industry domains. As a result, the existing solutions forefficiently identifying and recognizing entity relationships acrossdifferent industry domain dictionaries are extremely difficult at apractical level.

SUMMARY

Broadly speaking, selected embodiments of the present disclosure providea system, method, and apparatus for processing of inquiries to aninformation handling system capable of answering questions by using thecognitive power of the information handling system to deduce entityrelationships across a multiple knowledge databases or corpora usingcluster-based dictionary vocabulary lexicon which are weighted orscored. In selected embodiments, the information handling system may beembodied as a question answering (QA) system which receives and answersone or more questions from one or more users. To answer a question, theQA system has access to structured, semi-structured, and/or unstructuredcontent in a plurality of business documents (e.g., billing, customerorders, procedures, dealers, customer correspondence, credit, incidence,service, etc.) that are contained in one or more large knowledgedatabases (a.k.a., “corpus”) from different industry domains. Toidentify entity relationships in the different industry domains, the QAsystem identifies two or more different classifications or domains(a.k.a., business silos) for business documents. In addition, the QAsystem performs natural language processing (NLP) and analysis to thebusiness documents to identify or extract key terms, contexts, andconcepts, such as named entities, phrases, and/or terms in the businessdocuments which are stored in one or more domain entity dictionaries.The identified or extracted key terms, contexts, and concepts are thenclassified or assigned to each of the identified classifications ordomains. Within and/or across each identified classification or domain,the QA system applies semantic analysis to extract shallow and/or deeprelationships and assign corresponding relationship scores. Applying anormalized weighting algorithm to the relationship scores, the QA systemidentifies relationships between the terms and concepts of the two ormore different classifications or domains. The identified relationshipsmay also be ranked or filtered using a score threshold metric applied atthe QA system, thereby evaluating the identified relationships to selectthose having top ranked scores or that exceed the score thresholdmetric. In addition, the QA system may construct a model which specifiesthe relationships between the identified classifications or domains.With the contextual and weight driven mining techniques disclosed hereinfor dealing with unstructured and semi-structured corpus, entityrelationships across disparate knowledge databases or corpora may beefficiently extracted and correlated with minimal human supervision,thereby providing an automated approach for deducing relationshipsbetween ambiguous entity descriptions in different corpora.

The foregoing is a summary and thus contains, by necessity,simplifications, generalizations, and omissions of detail; consequently,those skilled in the art will appreciate that the summary isillustrative only and is not intended to be in any way limiting. Otheraspects, inventive features, and advantages of the present invention, asdefined solely by the claims, will become apparent in the non-limitingdetailed description set forth below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention may be better understood, and its numerousobjects, features, and advantages made apparent to those skilled in theart by referencing the accompanying drawings, wherein:

FIG. 1 depicts a network environment that includes a knowledge managerthat utilizes a knowledge base;

FIG. 2 is a block diagram of a processor and components of aninformation handling system such as those shown in FIG. 1;

FIG. 3 is a component diagram depicting various system components forassigning weights to dictionary vocabulary lexicon based on domaindictionary corpus and context dictionary corpus.

FIG. 4 is a component diagram depicting various system components forextracting entity relationship information across corpora.

FIG. 5 illustrates a simplified flow chart showing the logic fordeducing entity relationships across different knowledge databases orcorpora using cluster-based dictionary vocabulary lexicon which areweighted or scored.

DETAILED DESCRIPTION

The present invention may be a system, a method, and/or a computerprogram product. In addition, selected aspects of the present inventionmay take the form of an entirely hardware embodiment, an entirelysoftware embodiment (including firmware, resident software, micro-code,etc.) or an embodiment combining software and/or hardware aspects thatmay all generally be referred to herein as a “circuit,” “module” or“system.” Furthermore, aspects of the present invention may take theform of computer program product embodied in a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a dynamic or static random access memory(RAM), a read-only memory (ROM), an erasable programmable read-onlymemory (EPROM or Flash memory), a magnetic storage device, a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server or cluster of servers. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

FIG. 1 depicts a schematic diagram of one illustrative embodiment of aquestion/answer (QA) system 100 connected to a computer network 102. TheQA system 100 may include one or more QA system pipelines 100A, 100B,each of which includes a computing device 104 (comprising one or moreprocessors and one or more memories, and potentially any other computingdevice elements generally known in the art including buses, storagedevices, communication interfaces, and the like) for processingquestions received over the network 102 from one or more users atcomputing devices (e.g., 110, 120, 130). Over the network 102, thecomputing devices communicate with each other and with other devices orcomponents via one or more wired and/or wireless data communicationlinks, where each communication link may comprise one or more of wires,routers, switches, transmitters, receivers, or the like. In thisnetworked arrangement, the QA system 100 and network 102 may enablequestion/answer (QA) generation functionality for one or more contentusers. Other embodiments of QA system 100 may be used with components,systems, sub-systems, and/or devices other than those that are depictedherein.

In the QA system 100, the knowledge manager 104 may be configured toreceive inputs from various sources. For example, knowledge manager 104may receive input from the network 102, one or more knowledge bases orcorpora of electronic documents 106 or other data, a content creator108, content users, and other possible sources of input. In selectedembodiments, the knowledge base 106 may include structured,semi-structured, and/or unstructured content in a plurality of businessdocuments (e,g., billing, customer orders, procedures, dealers, customercorrespondence, credit, incidence, service, etc.) that are contained inone or more large knowledge databases or corpora from different industrydomains. The various computing devices (e.g., 110, 120, 130) on thenetwork 102 may include access points for content creators and contentusers. Some of the computing devices may include devices for a databasestoring the corpus of data as the body of information used by theknowledge manager 104 to generate answers to cases. The network 102 mayinclude local network connections and remote connections in variousembodiments, such that knowledge manager 104 may operate in environmentsof any size, including local and global, e.g., the Internet.Additionally, knowledge manager 104 serves as a front-end system thatcan make available a variety of knowledge extracted from or representedin documents, network-accessible sources and/or structured data sources.In this manner, some processes populate the knowledge manager with theknowledge manager also including input interfaces to receive knowledgerequests and respond accordingly.

In one embodiment, the content creator creates content in a document 107for use as part of a corpus of data with knowledge manager 104. Thedocument 107 may include any file, text, article, or source of data(e.g., scholarly articles, dictionary definitions, encyclopediareferences, and the like) for use in knowledge manager 104. Contentusers may access knowledge manager 104 via a network connection or anInternet connection to the network 102, and may input questions toknowledge manager 104 that may be answered by the content in the corpusof data. As further described below, when a process evaluates a givensection of a document for semantic content, the process can use avariety of conventions to query it from the knowledge manager. Oneconvention is to send a well-formed question. Semantic content iscontent based on the relation between signifiers, such as words,phrases, signs, and symbols, and what they stand for, their denotation,or connotation. In other words, semantic content is content thatinterprets an expression, such as by using Natural Language (NL)Processing. In one embodiment, the process sends well-formed questions(e.g., natural language questions, etc.) to the knowledge manager 104.Knowledge manager 104 may interpret the question and provide a responseto the content user containing one or more answers to the question. Insome embodiments, knowledge manager 104 may provide a response to usersin a ranked list of answers.

In some illustrative embodiments, QA system 100 may be the IBM Watson™QA system available from International Business Machines Corporation ofArmonk, N.Y., which is augmented with the mechanisms of the illustrativeembodiments described hereafter. The IBM Watson™ knowledge managersystem may receive an input question which it then parses to extract themajor features of the question, that in turn are then used to formulatequeries that are applied to the corpus of data stored in the knowledgebase 106. Based on the application of the queries to the corpus of data,a set of hypotheses, or candidate answers to the input question, aregenerated by looking across the corpus of data for portions of thecorpus of data that have some potential for containing a valuableresponse to the input question.

In order to answer a submitted question, the QA system 100 has access tothe large knowledge database 106 which contains textual information andelectronic documents 107 organized in different corpora. To retrievemeaningful information from the knowledge database 106, the knowledgemanager 104 may be configured analyze textual information by locatingand classifying elements in the textual information into pre-definedcategories, such as the names of persons, organizations, locations,expressions of times, quantities, monetary values, percentages, etc. Tothis end, the knowledge manager 104 may use an entity extraction process11, such as a named entity recognition (NER) process (also known asentity identification, entity chunking and entity extraction), to locateand classify elements from the textual information into pre-definedcategories, such as the names of persons, organizations, locations,expressions of times, quantities, monetary values, percentages, etc. Theentity extraction process 11 may use natural language (NL) processing toanalyze textual information in the corpora and extract entityinformation contained therein, such as named entities, phrases, urgentterms, and/or other specified terms. For example, the entity extractionprocess 11 may use a Natural Language Processing (NLP) routine toidentify specified entity information in the corpora, where “NLP” refersto the field of computer science, artificial intelligence, andlinguistics concerned with the interactions between computers and human(natural) languages. In this context, NLP is related to the area ofhuman-computer interaction and natural language understanding bycomputer systems that enable computer systems to derive meaning fromhuman or natural language input. The results of the entity extractionprocess 11 may be processed by the knowledge manager 104 with anassignment process 12 which assigns the extracted entity information toone or more business silos or clusters (e.g., an “order” silo, “inquiry”silo, or “billing” silo). Once the different business silos or clustersare populated with the extracted entity information, the knowledgemanager 104 may be configured to perform a relationship discovery andscoring process 13 on the contents of the different silos or clusters todeduce entity relationships across the different silos or clusters usinga variety of shallow and deep relationship extraction algorithms, andthen score the identified relationships. The identified relationshipsmay be further processed by the knowledge manager 104 to construct amodel of silo or cluster relationship information 109 that is stored inthe knowledge database 106 for use by the QA system 100 when answeringquestions 10.

In particular, a received question 10 may be processed by the IBMWatson™ QA system 100 which performs deep analysis on the language ofthe input question 10 and the language used in each of the portions ofthe corpus of data found during the application of the queries,including the cluster relationship information 109, using a variety ofreasoning algorithms. There may be hundreds or even thousands ofreasoning algorithms applied, each of which performs different analysis,e.g., comparisons, and generates a score. For example, some reasoningalgorithms may look at the matching of terms and synonyms within thelanguage of the input question and the found portions of the corpus ofdata. Other reasoning algorithms may look at temporal or spatialfeatures in the language, while others may evaluate the source of theportion of the corpus of data and evaluate its veracity.

The scores obtained from the various reasoning algorithms indicate theextent to which the potential response is inferred by the input questionbased on the specific area of focus of that reasoning algorithm. Eachresulting score is then weighted against a statistical model. Thestatistical model captures how well the reasoning algorithm performed atestablishing the inference between two similar passages for a particulardomain during the training period of the IBM Watson™ QA system. Thestatistical model may then be used to summarize a level of confidencethat the IBM Watson™ QA system has regarding the evidence that thepotential response, i,e., candidate answer, is inferred by the question.This process may be repeated for each of the candidate answers until theIBM Watson™ QA system identifies candidate answers that surface as beingsignificantly stronger than others and thus, generates a final answer,or ranked set of answers, for the input question. The QA system 100 thengenerates an output response or answer 20 with the final answer andassociated confidence and supporting evidence. More information aboutthe IBM Watson™ QA system may be obtained, for example, from the IBMCorporation website, IBM Redbooks, and the like. For example,information about the IBM Watson™ QA system can be found in Yuan et al.,“Watson and Healthcare,” IBM developerWorks, 2011 and “The Era ofCognitive Systems: An Inside Look at IBM Watson and How it Works” by RobHigh, IBM Redbooks, 2012.

Types of information handling systems that can utilize QA system 100range from small handheld devices, such as handheld computer/mobiletelephone 110 to large mainframe systems, such as mainframe computer170. Examples of handheld computer 110 include personal digitalassistants (PDAs), personal entertainment devices, such as MP3 players,portable televisions, and compact disc players. Other examples ofinformation handling systems include pen, or tablet, computer 120,laptop, or notebook, computer 130, personal computer system 150, andserver 160. As shown, the various information handling systems can benetworked together using computer network 102. Types of computer networkat can be used to interconnect the various information handling systemsinclude Local Area Networks (LANs). Wireless Local Area Networks(WLANs), the Internet, the Public Switched Telephone Network (PSTN),other wireless networks, and any other network topology that can be usedto interconnect the information handling systems. Many of theinformation handling systems include nonvolatile data stores, such ashard drives and/or nonvolatile memory. Some of the information handlingsystems may use separate nonvolatile data stores (e.g., server 160utilizes nonvolatile data store 165, and mainframe computer 170 utilizesnonvolatile data store 175). The nonvolatile data store can be acomponent that is external to the various information handling systemsor can be internal to one of the information handling systems. Anillustrative example of an information handling system showing anexemplary processor and various components commonly accessed by theprocessor is shown in FIG. 2.

FIG. 2 illustrates information handling system 200, more particularly, aprocessor and common components, which is a simplified example of acomputer system capable of performing the computing operations describedherein. Information handling system 200 includes one or more processors210 coupled to processor interface bus 212. Processor interface bus 212connects processors 210 to Northbridge 215, which is also known as theMemory Controller Hub (MCH). Northbridge 215 connects to system memory220 and provides a means for processor(s) 210 to access the systemmemory. In the system memory 220, a variety of programs may be stored inone or more memory device, including an entity relationship module 221which may be invoked to deduce and score entity relationships acrossdisparate corpora. Graphics controller 225 also connects to Northbridge215. In one embodiment, PCI Express bus 218 connects Northbridge 215 tographics controller 225. Graphics controller 225 connects to displaydevice 230, such as a computer monitor.

Northbridge 215 and Southbridge 235 connect to each other using bus 219.In one embodiment, the bus is a Direct Media Interface (DMI) bus thattransfers data at high speeds in each direction between Northbridge 215and Southbridge 235. In another embodiment, a Peripheral ComponentInterconnect (PCI) bus connects the Northbridge and the Southbridge.Southbridge 235, also known as the I/O Controller Hub (ICH) is a chipthat generally implements capabilities that operate at slower speedsthan the capabilities provided by the Northbridge. Southbridge 235typically provides various busses used to connect various components.These busses include, for example, PCI and PCI Express busses, an ISAbus, a System Management Bus (SMBus or SMB), and/or a Low Pin Count(LPC) bus. The LPC bus often connects low-bandwidth devices, such asboot ROM 296 and “legacy” I/O devices (using a “super I/O” chip). The“legacy” I/O devices (298) can include, for example, serial and parallelports, keyboard, mouse, and/or a floppy disk controller. Othercomponents often included in Southbridge 235 include a Direct MemoryAccess (DMA) controller, a Programmable Interrupt Controller (PIC), anda storage device controller, which connects Southbridge 235 tononvolatile storage device 285, such as a hard disk drive, using bus284.

ExpressCard 255 is a slot that connects hot-pluggable devices to theinformation handling system. ExpressCard 255 supports both PCI Expressand USB connectivity as it connects to Southbridge 235 using both theUniversal Serial Bus (USB) the PCI Express bus. Southbridge 235 includesUSB Controller 240 that provides USB connectivity to devices thatconnect to the USB. These devices include webcam (camera) 250, infrared(JR) receiver 248, keyboard and trackpad 244, and Bluetooth device 246,which provides for wireless personal area networks (PANs). USBController 240 also provides USB connectivity to other miscellaneous USBconnected devices 242, such as a mouse, removable nonvolatile storagedevice 245, moderns, network cards, ISDN connectors, fax, printers, USBhubs, and many other types of USB connected devices. While removablenonvolatile storage device 245 is shown as a USB-connected device,removable nonvolatile storage device 245 could be connected using adifferent interface, such as a Firewire interface, etc.

Wireless Local Area Network (LAN) device 275 connects to Southbridge 235via the PCI or PCI Express bus 272. LAN device 275 typically implementsone of the IEEE 802.11 standards for over-the-air modulation techniquesto wireless communicate between information handling system 200 andanother computer system or device. Extensible Firmware Interface (EFI)manager 280 connects to Southbridge 235 via Serial Peripheral Interface(SPI) bus 278 and is used to interface between an operating system andplatform firmware. Optical storage device 290 connects to Southbridge235 using Serial ATA (SATA.) bus 288. Serial ATA adapters and devicescommunicate over a high-speed serial link. The Serial ATA bus alsoconnects Southbridge 235 to other forms of storage devices, such as harddisk drives. Audio circuitry 260, such as a sound card, connects toSouthbridge 235 via bus 258. Audio circuitry 260 also providesfunctionality such as audio line-in and optical digital audio in port262, optical digital output and headphone jack 264, internal speakers266, and internal microphone 268. Ethernet controller 270 connects toSouthbridge 235 using a bus, such as the PCI or PCI Express bus.Ethernet controller 270 connects information handling system 200 to acomputer network, such as a Local Area Network (LAN), the Internet, andother public and private computer networks.

While FIG. 2 shows one information handling system, an informationhandling system may take many forms, some of which are shown in FIG. 1.For example, an information handling system may take the form of adesktop, server, portable, laptop, notebook, or other form factorcomputer or data processing system. In addition, an information handlingsystem may take other form factors such as a personal digital assistant(PDA), a gaming device, ATM machine, a portable telephone device, acommunication device or other devices that include a processor andmemory. In addition, an information handling system need not necessarilyembody the north bridge/south bridge controller architecture, as it willbe appreciated that other architectures may also be employed.

FIGS. 3-5 depict an approach that can be executed on an informationhandling system to identify entity relationships across disparatecorpora for use in answering questions 10 being presented to a knowledgemanagement system, such as QA system 100 shown in FIG. 1. This approachcan be included within the QA system 100 or provided as a separateentity relationship identification system, method, or module. Whereverimplemented, the disclosed entity relationship identification schememines unstructured and semi-structured documents and text from aplurality of knowledge bases or corpora for information about elatedentities to obtain an improved understanding of the entirety of thecorpora to assist with generating an answer to a presented question. Themined information includes the presence of any key terms, phrases, ornamed entities in the question which may be extracted from differentdatabases by using NLP techniques. In addition, the entity relationshipidentification scheme assigns extracted entity information to one ormore specific business silos or clusters, and then applies semanticanalysis to extract and score shallow and/or deep relationships betweenthe business silos or clusters. Based on the scored or rankedrelationships, a model may be constructed which specifies relationshipsbetween individual entities referenced or contained within thecluster/silo information.

To provide additional details for an improved understanding of selectedembodiments of the present disclosure, reference is now made to FIG. 3which depicts a component diagram 300 of various system components forassigning weights to dictionary vocabulary lexicon based on domaindictionary corpus and context dictionary corpus. The system componentsshown in FIG. 3 may be used to discover entity relationships in theknowledge corpora 106 that are used to answer a question request (e.g.,question 10) presented for processing to a cognitive system 100, such asan IBM Watson™ QA system or other natural language question answeringsystem shown in FIG. 1.

In selected embodiments, the system component diagram 300 may be used todiscover entity relationships across different business data silos orsub-domains that exist within a business entity or domain. Examplesub-domains may include order fulfillment, billing, wellness care,service assurance, warehouse management, accounting, technical support,etc. With such sub-domains, it will be appreciated that each sub-domainmay develop its own terminology and data models, reflected in bothstructured and unstructured information stores. For example, a term orentity called “jeopardy” in the “order fulfillment” sub-domain is theprocess of resolving a failed order, but in the “accounting” and“wellness” sub-domains, the term means something completely different.Since it is not always possible to impose a common and normalizedontology across business silos to enforce consistent terminology ormodeling, the result is that inconsistencies can develop acrosscommunities which hamper efforts to glean larger-scale analytic orpredictive conclusions. Even when an entity and associated attributesare the same across business silos, such as a “Customer” and associatedentity such as “account or ID number,” the data models may not useforeign key relationships to record this equivalence.

Rather than extracting relationship information from an aggregation ofall data across all of the silos to form a corpus that represents thebusiness in its entirety into one corpus, the system component diagram300 may be used to separate the structured data analysis of the businessdocuments in each document corpus 310 into a two-phase process whichfinds relationships within each sub-domain first, and then subsequentlyfinding linkages across sub-domains to create a more precise model. Asillustrated, each document corpus 310 may include a plurality ofelectronic documents 311, a document or knowledge database 312, one ormore webpages 313, and the like. Information from each document in thecorpus 310 is processed at a knowledge manager or processor 316 using adomain concepts corpus 315 and a context or scenarios corpus 317 tocalculate and assign a weight for the associated context for the eachrecord in the index. As a preliminary step in specifying weights, thepre-processing module 318 extracts key features and terms from eachdocument in the source corpus 310 for storage in the vocabularydictionary 320. In extracting the terms (e.g., Billing, Catalog, Credit,Customer, Dealer, Order Management, Order, Problem, Provisioning,Severity), the pre-processing module 318 may also assemble a. postingslist that records in which document each term appears. For example, thepostings list for the “Billing” terms include documents 7578, 83635,99604, 7578, 89800. All posting lists taken together form the “postings”322.

For each extracted term in the dictionary 320, the knowledgemanager/processor 316 and/or pre-processing module 318 uses informationfrom the domain concepts corpus 315 to match and associate the domainunder which the term would fit in addition, the knowledgemanager/processor 316 and/or pre-processing module 318 uses informationfrom the context/scenarios corpus 317 to match and associate thecontexts) or scenarios(s) in which the term will be needed.

At the weight assignment module 324, the weight for each term may becomputed using any desired weighting algorithm. For example, the weightfor a term T in the extracted dictionary 320 may be computed based onthe term D in the domain concepts corpus 315 and from the term C in thecontext/scenarios corpus 317 using the equation:

Computed Weight=(P(T)×P(D|T))/(P(D))+(P(T)×P(C|T))/(P(C)).

In addition, the weight assignment module 324 may compute normalizedweight values=Computed Weight*(Total # of documents in Corpus that wereprocessed)/(Total # of documents in Corpus that were processed+Total #of documents in Corpus that were rejected). Based on these computations,one or more related entities and corresponding assigned weights 326 areextracted for each term in the dictionary 320. For example, the domainconcepts corpus 315 and context/scenarios corpus 317 are used to extractthe “Billing Inquiry” and “Collections” entities as being related to the“Billing” term in the dictionary 320 with a normalized weight of 0.45.

By using the domain concepts corpus 315 and context/scenarios corpus 317in a two-phase process to extract and score related entity information326, relationships within a sub-domain may be identified before findinglinkages across sub-domains, thereby creating a model that revealslinkages or relationships between entities across sub-domains that arenot expressed in any business documents or structured data models. As aresult, lexical mappings across business sub-domains can be produced asa translation resource that is generally useful for any data modeling orunstructured data analysis activity. This enables the discovery ofhidden threads between the same real-world entities that are shuttledacross myriad business activities and flows that are managed by multipledepartments or functional units across a large organization, each ofwhich may use distinct terminology when referring to that entity.

With the depicted system components 300, a variety of different entityrelationships and associated lexical mappings may be discovered. Inselected embodiments, relationships between underspecified predicateterms can be discovered in cases where the same term is used indifferent business areas by using the specified context for the term tomap the term to an entity. For example, billing department documentswill frequently use the term “Inquiry” which, in this context, refers toa billing inquiry which is received from an “account” that is abusinesses that purchased the company's products. However, a technicalsupport department documents may also use the term “Inquiry” which, inthis context, is typically made by people or actual users using theservice who are following up on an outage report. When these documentsare ingested and a conventional relationship generalization process runsagainst them, the frame extraction may not be able to formulate anyreliable conclusion at all since the arguments to “Inquiry” vary. Toaddress this, the depicted system components 300 may implement atwo-phase method which first creates relationship clusters for thebilling documents before mapping those relationships across sub-domainsto the clusters formed from technical support documents.

In other embodiments, the depicted system components 300 may be used todiscover entity relationships when different guises are used for thesame entity. For example, the billing department may have an entity type“company” that contains a certain inventory of tokens, while thetechnical support department may have an entity type called “reportingaccount” that contains a subset of the same tokens. To discover thisconnection, the depicted system components 300 may implement aclustering method to discover that those entity fields refer to the sameentity. Once such mappings are known, cross-links between entities indifferent clusters can be used to spread the weights of otherrelationships or for similar calculations that were previouslyunsupported.

The depicted system components 300 may also be used to discover entityrelationships by linking entities across different business silos whenbusiness entities participate in different relationships within variousphases of their engagement with the enterprise. For example, attributesappropriate for one phase, such as creditworthiness calculations, do notnecessarily propagate to the models of other business silos, vetanalytic conclusions that cross functional boundaries within thebusiness may benefit from chaining together the relations of the entityin a sequence of phases. To make this connection, the depicted systemcomponents 300 may be used to develop both within-cluster links andcross-cluster links between entities in different business silos.

The depicted system components 300 may also be used to discover entityrelationships by performing context mapping for each entity byidentifying or inferring context information from a business silo andstoring the additional context information with a special relationship.For example, an “Inquiry” term will have a context of account in case of“Billing,” but will have a context of “Order Management” in case ofcustomer order. To perform this context mapping, the depicted systemcomponents 300 may be used to map additional context information foreach entity.

To provide additional details for an improved understanding of selectedembodiments of the present disclosure, reference is now made to FIG. 4which depicts a component diagram 400 of various system components andprocessing steps for extracting entity relationship information acrosscorpora. The processing shown in FIG. 4 is performed by a cognitivesystem 100, such as an IBM Walsott™ QA system or other natural languagequestion answering system shown in FIG. 1, discovering entityrelationships in structured, semi-structured, and/or unstructuredcontent stored in the knowledge corpora 106 from different industrydomains that are used to answer a question request (e.g., question 10)presented to the cognitive system 100.

To identify entity relationships in the different industry domains, thecognitive system 100 first discovers entity relationships by extractingnamed entity and related entity information from different clusters orbusiness silos. In selected example embodiments, the entityrelationships may be discovered by using system components 300 (such asshown in FIG. 3) to assign weights to dictionary vocabulary lexiconbased on domain dictionary corpus and context dictionary corpus. Theprocessing to extract entity relationships may be performed at theentity extraction process 11 (FIG. 1) or other NLP routine. In selectedembodiments, the extracted entity information may be named entities401-404, such as a first entity E1=“Order Status” 401, a second entityE2=“Customer” 402, a third entity E3=“Incidents” 403, or a fourth entityE4=“Service” 404. Stated more generally, the extracted named entitiesmay be referred to as identifies E_(i) . . . E_(n).

Using the extracted entity relationship information (E_(i) . . .E_(n).), the cognitive system 100 identifies and assigns relatedentities to a specific business silo or cluster 411-413, as indicated bythe assignment step 410. The processing to identify and assign relatedentities to specific clusters or business silos 411-413 may be performedat the assignment process 12 (FIG. 1) or other NLP routine. For example,a plurality of business silos (B₁ . . . B_(M)) may be defined, such as afirst “Order” business silo or cluster B₁ 411, a second “Inquiry”business silo or cluster B₂ 412, or a third “Billing” business silo orcluster B₃ 413. With the defined business silos (B₁ . . . B_(M)), thefirst “Order Status” entity E1 401 may be assigned to the “Order” and“Inquiry” business silos 411-412. In addition, the second “Customer”entity E2 402 may be assigned to the “Order,” “Inquiry,” and “Billing”business silos 411-413. Similarly, the third “Incidents” entity E3 403may be assigned to the “Inquiry” business silo 412, and the fourth“Service” entity E4 404 may be assigned to the “Inquiry” and “Billing”business silos 412-413.

Once the extracted entities (E_(i) . . . E_(n).) are assigned to thedifferent business silos or clusters (B₁ . . . B_(M)), the cognitivesystem 100 processes the entities in each silo/cluster by performingsemantic analysis to extract shallow and/or deep relationships withentities in other silos or clusters and assign correspondingrelationship scores. The entity processing may be performed at therelationship discovery and scoring process 13 (FIG. 1) or other NLProutine. For each entity (e.g., E1, E2, . . . ) in a specified silo orcluster (e.g., B₁), the entity processing module 420 extracts andidentifies one or more associated entities (e.g., E1) in an adjacentsilo or cluster (e.g., B₂) by searching the data corpus 422 to extract ashallow relationship (e.g., R₁ 414) with sentences containing theassociated entities (e.g., E1 from cluster B₁ and E1 from cluster B₂).In the next silo or cluster (e.g., B₂), the process is repeated for eachentity (e.g., E2) by the entity processing module 420 which extracts andidentifies one or more associated entities (e.g., E4) in an adjacentsilo or cluster (e.g., B_(M)) by searching the data corpus 422 toextract a shallow relationship (e.g., R₂ 415) with sentences containingthe associated entities (e.g., E1 from cluster B₂ and E4 from clusterB_(M)). Stated more generally, the entity processing module 420 mayinclude a shallow relationship extractor which processes each selectedentity E_(i) from a first silo or cluster (B_(J)) to extract andassociate entity information E_(i) through E_(n) from an adjacent siloor cluster (B_(J+1)) by searching the data corpus 422 to extract shallowrelationships (R_(k)) with sentences containing the selected entityE_(i) from the first silo or cluster (B_(J)) and the associated entityE_(i) from an adjacent silo or cluster (B_(J+1)). At the entityprocessing module 420, each extracted shallow relationship (R_(k)) maybe assigned a first relationship score (e.g., Score1).

In addition or in the alternative, the entity processing module 420 mayperform a similar silo-based processing of entities to extract deeprelationship information for related entities in different silos orclusters. To this end, the entity processing module 420 may include adeep relationship extractor for processing each entity (e.g., E1, E2, .. . ) in a specified silo or cluster (e.g., B₁) by searching the datacorpus 422 to perform deep syntactic and semantic analysis on extractedpassages. Based on the results of the deep relationship extraction, theentity processing module 420 may assign a second relationship score(e.g., Score2) to each extracted deep relationship, such as by using aninverse-document-frequency (IDF) or Passage Score techniques. Statedmore generally, the entity processing module 420 may use any desiredrelationship extraction process to identify and score entityrelationships across the different business silos or clusters (B₁ . . .B_(M)).

Once the individual entity relationships are identified and scored, alist of normalized scores 424 is generated by the entity processingmodule 420. To generate the list of normalized scores 424, a normalizedscore S_(K) is computed for each identified entity relationship R_(K),and the resulting list 424 may include pairings of R₁:S₁, R₂:S₂, R₃:S₃,etc.

To filter out weakly linked entity relationship information, the list ofnormalized scores 424 may be processed by the filter module 426 whichapplies one or more threshold criteria. For example, the filter module426 may be configured to evaluate each of the normalized scores S_(K)against a specified score threshold (e.g., 0.75 or 0.8) so that onlyentity relationships having normalized scores that meet or exceed thespecified score threshold are included in a list of related clusters428. From the assembled list 428, a model of the cluster relationshipsmay be constructed, serialized, and stored in the cluster relationshipdatabase 430. In this way, the computed relationship scores (e.g.,Score1, Score2, etc.) may be used to determine the degree ofrelationship between entities in different clusters or silos. And if thedegree of relationship meets a threshold requirement, then the resultingcluster relationships (e.g., cluster B₁ is related to cluster B₂,cluster B₂ is related to cluster B₅ cluster B₁ is related to cluster B₇,etc.) may be captured in the cluster relationship model 430.

To provide additional details for an improved understanding of selectedembodiments of the present disclosure, reference is now made to FIG. 5which depicts a simplified flow chart 500 showing the logic for deducingentity relationships across different knowledge databases or corporausing cluster-based dictionary vocabulary lexicon which are weighted orscored. The processing shown in FIG. 5 may be performed by a cognitivesystem, such as an IBM Watson™ QA system or other natural languagequestion answering system shown in FIG. 1, when ingesting structured,semi-structured, and/or unstructured content in business documentscontained in a plurality of knowledge databases from different industrydomains. FIG. 5 processing commences at 501 whereupon, at step 502, theprocess extracts named entities, relationships, and co-references fromthe business documents. The processing at step 502 may be performed atthe QA system 100 by the en extraction process 11 or other NLP questionanswering system. As described herein, a Natural Language ProcessingTLP) routine may be used to perform entity extraction processing on thebusiness documents, where “NLP” refers to the field of computer science,artificial intelligence, and linguistics concerned with the interactionsbetween computers and human (natural) languages. In this context, NLP isrelated to the area of human-computer interaction and natural languageunderstanding by computer systems that enable computer systems to derivemeaning from human or natural language input.

At step 503, the extracted entity information is assigned to one or moreclusters or business silos. The processing at step 503 may be performedat the QA system 100 by the assignment process 12 or other NLP questionanswering system. As described herein, the assignment process mayclassify or assign each of the extracted entities (E_(i) . . . E_(n).)to one or more business silos or clusters (B_(i) . . . B_(M)). As aresult of the assignment processing step 503, each silo or cluster mayinclude information identifying one or more of the extracted entities.

At step 504, each entity (e.g., E_(i)) in a given cluster (e.g., B_(j))is processed to extract entity relationships with other entities (e.g.,E_(i)-E_(n)) in an adjacent cluster (e.g., B_(j+1)). The processing atstep 504 may be performed at the QA system 100 by the relationshipdiscovery and scoring process 13 or other NLT question answering system.As described herein, the relationship extraction process proceedsthrough each entity in the current cluster (e.g., B_(j)) by using the anegative outcome from detection step 505 to increment the cluster count(step 506) until the last cluster is reached, as indicated by anaffirmative outcome to detection step 505.

At step 508, the extracted relationship information for all entities inall clusters is processed to extract shallow relationship information(SR_(K)). The processing at step 508 may be performed at the QA system100 by the relationship discovery and scoring process 13 or other NLPquestion answering system which searches the data corpus for sentencescontaining the related entity information. Based on the results of thecorpus search at step 508, a first score is assigned to each identifiedshallow relationship using any desire scoring technique.

At step 510, the extracted relationship information for all entities inall clusters is processed to extract deep relationship information(DR_(K)). The processing at step 510 may be performed at the QA system100 by the relationship discovery and scoring process 13 or other NLPquestion answering system which performs deep syntactic and semanticanalysis on extracted passages from the data corpus containing therelated entity information. Based on the results of the corpus search atstep 510, a second score is assigned to each identified deeprelationship using any desire scoring technique.

At step 512, the first and second scores for the shallow and deeprelationships are used to compute a list of normalized scores (S_(K)) iscomputed for each identified entity relationship (R_(K)). The processingat step 512 may be performed at the QA system 100 by the relationshipdiscovery and scoring process 13 or other NLP question answering systemwhich may generate a list of entity relationships paired with theircorresponding normalized scores (e.g., R₁:S₁, R₂:S₂, R₃:S₃, etc.).

At step 514, the list of normalized scores may be filtered or rankedagainst one or more threshold criteria. The processing at step 514 maybe performed at the QA system 100 by the relationship discovery andscoring process 13 or other NLP question answering system which appliesa threshold test to the normalized scores (S_(K)) to filter out weaklylinked entity relationship information. In this way, a list of relatedclusters may be created for each entity relationship whose normalizedscore passes the threshold test.

At step 516, the assembled list of related dusters is used to constructa model of the cluster relationships. The processing at step 516 may beperformed at the QA system 100 by the relationship discovery and scoringprocess 13 or other NLP question answering system which may serializethe assembled list of related clusters and store this as a model of thecluster relationships in a cluster relationship database.

By now, it will be appreciated that there is disclosed herein a system,method, apparatus, and computer program product for identifying, with aninformation handling system having a processor and a memory, clusterrelationships for searching across different corpora based on wordclassifications extracted from business documents stored in a pluralityof corpora, where the business documents may be billing, customerorders, procedures, dealers, customer correspondence, credit, incidence,and/or service documents. As disclosed, the system, method, apparatus,and computer program product identify a plurality of different clusterclassifications for business documents stored in the plurality ofcorpora, where each cluster classification corresponds to a differentcorpora. In addition, entity information from the business documents isclassified into the plurality of different cluster classifications. Theclassification step may include performing natural language processing(NLP) analysis of the business documents to extract named entityinformation from the business documents. In other embodiments, theentity information may be classified by extracting named entities,terms, contexts, and concepts from the business documents, and thenassigning the named entities, terms, contexts, and concepts to at leastone of the plurality of different cluster classifications. By applyingsemantic analysis, such as shallow and deep semantic analysis methods,to the classified entity information, entity relationships areidentified between entity information classified in the differentcluster classifications. In addition, one or more scores are determinedor computed for each identified entity relationship. The scoring processmay include applying a normalized weighting algorithm to the one or morescores to generated normalized scores for each identified entityrelationship. After applying a threshold test to the (normalized) scoresto identify each entity relationship having a normalized score thatmeets or exceeds the threshold test, the resulting scores (e.g., thenormalized scores that meet or exceed the threshold test) may then beused to identify a cluster relationship between at least two clusterclassifications, and a model may be constructed of each identifiedcluster relationship between the at least two cluster classifications.Based on the identified cluster relationship, the information handlingsystem may search first and second corpora corresponding to the clusterclassifications having the identified cluster relationship, therebyexpanding the corpora search capabilities to include related corpora.

While particular embodiments of the present invention have been shownand described, it will be obvious to those skilled in the art that,based upon the teachings herein, that changes and modifications may bemade without departing from this invention and its broader aspects.Therefore, the appended claims are to encompass within their scope allsuch changes and modifications as are within the true spirit and scopeof this invention. Furthermore, it is to be understood that theinvention is solely defined by the appended claims. It will beunderstood by those with skill in the art that if a specific number ofan introduced claim element is intended, such intent will be explicitlyrecited in the claim, and in the absence of such recitation no suchlimitation is present. For non-limiting example, as an aid tounderstanding, the following appended claims contain usage of theintroductory phrases “at least one” and “one or more” to introduce claimelements. However, the use of such phrases should not be construed toimply that the introduction of a claim element by the indefinitearticles “a” or “an” limits any particular claim containing suchintroduced claim element to inventions containing only one such element,even when the same claim includes the introductory phrases “one or more”or “at least one” and indefinite articles such as “a” or “an”; the sameholds true for the use in the claims of definite articles,

1-8. (canceled)
 9. An information handling system comprising: one ormore processors; a memory coupled to at least one of the processors; aset of instructions stored in the memory and executed by at least one ofthe processors to identify cluster relationships for searching across aplurality of corpora, wherein the set of instructions perform actionsof: identifying, by the system, a plurality of different clusterclassifications for corresponding plurality of corpora; classifying, bythe system, entity information from documents stored in the plurality ofcorpora into the plurality of different cluster classifications;applying semantic analysis, by the system, to identify entityrelationships between entity information classified in the plurality ofdifferent cluster classifications; determining, by the system, one ormore scores for each identified entity relationship; identifying, by thesystem, a cluster relationship between at least two clusterclassifications based on the one or more scores for each identifiedentity relationships; and searching, by the information handling system,at least first and second corpora corresponding to the at least twocluster classifications having the identified cluster relationship. 10.The information handling system of claim 9, wherein classifying entityinformation from the documents comprises performing, by the system, anatural language processing (NLP) analysis of the documents, wherein theNLP analysis extracts named entity information from the documents. 11.The information handling system of claim 9, wherein the documents areselected from a group comprising billing, customer orders, procedures,dealers, customer correspondence, credit, incidence, and servicedocuments.
 12. The information handling system of claim 9, whereinclassifying entity information comprises: extracting named entities,terms, contexts, and concepts from the documents, and assigning thenamed entities, terms, contexts, and concepts to at least one of theplurality of different cluster classifications.
 13. The informationhandling system of claim 9, wherein applying semantic analysis comprisesapplying shallow and deep semantic analysis methods to identify entityrelationships between the plurality of different clusterclassifications.
 14. The information handling system of claim 9, whereindetermining one or more scores comprises applying a normalized weightingalgorithm to the one or more scores to generate normalized scores foreach identified entity relationship.
 15. The information handling systemof claim 14, further comprising applying a threshold test to thenormalized scores to identify each identified entity relationship havinga normalized score that meets or exceeds the threshold test.
 16. Theinformation handling system of claim 15, wherein the actions furthercomprise constructing a model which specifies cluster relationshipswithin the plurality of different cluster classifications.
 17. Acomputer program product stored in a computer readable storage medium,comprising computer instructions that, when executed by an informationhandling system, causes the information handling system to identifycluster relationships for searching across a plurality of corpora byperforming actions comprising: identifying, by the system, a pluralityof different cluster classifications for a corresponding plurality ofcorpora; classifying, by the system, entity information from documentsstored in the plurality of corpora into the plurality of differentcluster classifications; applying semantic analysis, by the system, toidentify entity relationships between entity information classified inthe plurality of different cluster classifications; determining, by thesystem, one or more scores for each identified entity relationship;identifying, by the system, a cluster relationship between at least twocluster classifications based on the one or more scores for eachidentified entity relationships; and searching, by the informationhandling system, at least first and second corpora corresponding to theat least two cluster classifications having the identified clusterrelationship.
 18. The computer program product of claim 17, whereinclassifying entity information comprises: extracting named entities,terms, contexts, and concepts from the documents stored in the pluralityof corpora, and assigning the named entities, terms, contexts, andconcepts to at least one of the plurality of different clusterclassifications.
 19. The computer program product of claim 17, whereinapplying semantic analysis comprises applying shallow and deep semanticanalysis methods to identify entity relationships between the pluralityof different cluster classifications.
 20. The computer program productof claim 17, wherein determining one or more scores comprises applying anormalized weighting algorithm to the one or more scores to generatenormalized scores for each identified entity relationship, and applyinga threshold test to the normalized scores to identify each entityrelationship having a normalized score that meets or exceeds thethreshold test.